Computer-Aided Medical Diagnosis Using Bayesian Classifier - Decision Support System for Medical Diagnosis

This study employs a Bayesian framework to construct a Web-based decision support system for medical diagnosis. The purpose is to help users (patients and physicians) with issues pertinent to medical diagnosis decisions and to detect diseases with highest probability through the Bayesian framework. Users could perform a more accurate diagnosis with the prior/conditional probabilities obtained from selected data sets and compute the posterior probability using the Bayes theorem. The proposed system identifies diseases by analyzing symptoms or by analyzing medical test results. Currently the system detects different types of diseases that people suffer in their day-to-day lives (general diseases) with an average detection accuracy of 92.56%. System also detects complex diseases (e.g.: heart disease - 83.67%, breast cancer - 80.98%, liver disorders - 79.43%, lung cancer - 71.00%, primary tumor - 78.02%, etc.) based on the analysis of the medical test results. The proposed system enhances the quality, accuracy and efficiency of decisions in medical diagnosis since the use of Bayesian theorem allows this system to offer more accurate platform than the conventional systems. Other than that this web-based system provides value-added services in conjunction with CAD system, such as; e-Chat & e-Channeling. More importantly, the targeted user group will be able to access the system as a software element freely and quickly. In this way the goal of this study – which is to provide a web-based medical diagnosis system is effectively achieved.KEYWORDS: Bayesian framework, medical decision support systems, computer-aided medical diagnosis, probability distributions, disease

Prediction of Heart Disease Using Machine Learning Algorithms

The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification

Mobile-Bayesian Diagnostic System for Childhood Infectious Diseases

About 5.9 million children under the age of 5 died in 2015, Preterm birth, delivery complications and infections source a great number of neonatal deaths. the Sustainable Development goals (SDGs) 3.2 is to end preventable deaths of newborns and children under 5 years of age, with a target to reduce neonatal mortality to at least 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births in all countries. However quality and accessible healthcare service is essential to achieve this goal whereas most undeveloped and developing countries still have poor access to quality healthcare. with the emergences on mobile computing and telemedicine, this work provide diagnostics alternative for childhood infectious diseases using Naïve Bayesian classier which has been proven to be efficient in handling uncertainty as regards learning of incomplete data. In this research, sample data was collected from hospitals to model a pediatric system using Naïve Bayes classifier, which produce a 70% accuracy level suitable for a decision support system. The model was also integrated into a SMS platform to enable ease of usage

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Information technologies for pain management

Millions of people around the world suffer from pain, acute or chronic and this raises the importance of its screening, assessment and treatment. The importance of pain is attested by the fact that it is considered the fifth vital sign for indicating basic bodily functions, health and quality of life, together with the four other vital signs: blood pressure, body temperature, pulse rate and respiratory rate. However, while these four signals represent an objective physical parameter, the occurrence of pain expresses an emotional status that happens inside the mind of each individual and therefore, is highly subjective that makes difficult its management and evaluation. For this reason, the self-report of pain is considered the most accurate pain assessment method wherein patients should be asked to periodically rate their pain severity and related symptoms. Thus, in the last years computerised systems based on mobile and web technologies are becoming increasingly used to enable patients to report their pain which lead to the development of electronic pain diaries (ED). This approach may provide to health care professionals (HCP) and patients the ability to interact with the system anywhere and at anytime thoroughly changes the coordinates of time and place and offers invaluable opportunities to the healthcare delivery. However, most of these systems were designed to interact directly to patients without presence of a healthcare professional or without evidence of reliability and accuracy. In fact, the observation of the existing systems revealed lack of integration with mobile devices, limited use of web-based interfaces and reduced interaction with patients in terms of obtaining and viewing information. In addition, the reliability and accuracy of computerised systems for pain management are rarely proved or their effects on HCP and patients outcomes remain understudied. This thesis is focused on technology for pain management and aims to propose a monitoring system which includes ubiquitous interfaces specifically oriented to either patients or HCP using mobile devices and Internet so as to allow decisions based on the knowledge obtained from the analysis of the collected data. With the interoperability and cloud computing technologies in mind this system uses web services (WS) to manage data which are stored in a Personal Health Record (PHR). A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness of the proposed computerised monitoring system. The six weeks RCT evidenced the advantages provided by the ubiquitous access to HCP and patients so as to they were able to interact with the system anywhere and at anytime using WS to send and receive data. In addition, the collected data were stored in a PHR which offers integrity and security as well as permanent on line accessibility to both patients and HCP. The study evidenced not only that the majority of participants recommend the system, but also that they recognize it suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented treatments with reduced therapist time. The study also revealed that the guidance of HCP at the beginning of the monitoring is crucial to patients' satisfaction and experience stemming from the usage of the system as evidenced by the high correlation between the recommendation of the application, and it suitability to improve pain management and to provide medical information. There were no significant differences regarding to improvements in the quality of pain treatment between intervention group and control group. Based on the data collected during the RCT a clinical decision support system (CDSS) was developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer) with an imputation model based on linear regression. This system resulted in fully accuracy related to decisions suggested by the system compared with the medical diagnosis, and therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems capability to deal with different complex data sources with varied complexities and accuracies, an innovative model was proposed. This model is characterized by a qualitative analysis stemming from the data fusion method combined with a quantitative model based on the comparison of the standard deviation together with the values of mathematical expectations. This model aimed to compare the effects of technological and pen-and-paper systems when applied to different dimension of pain, such as: pain intensity, anxiety, catastrophizing, depression, disability and interference. It was observed that pen-and-paper and technology produced equivalent effects in anxiety, depression, interference and pain intensity. On the contrary, technology evidenced favourable effects in terms of catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy to implement and low time and resources consuming. Further work is needed to evaluate the proposed system to follow up participants for longer periods of time which includes a complementary RCT encompassing patients with chronic pain symptoms. Finally, additional studies should be addressed to determine the economic effects not only to patients but also to the healthcare system